Class imbalance problem using a hybrid ensemble approach
Shaza M. Abd Elrahman, Ajith Abraham · International Journal of Hybrid Intelligent Systems · 2016
This paper proposes a comparative study that investigates the effects of using resampling (undersampling and oversampling) methods with homogenous ensemble methods Bagging and AdaBoost in imbalanced data sets. We presented a hybrid ensemble approach that combined multi resampling by integrating both undersampling and oversampling to get benefits and reduces drawbacks caused by each of them. The proposed approach has improved the performance even those most sensitive to imbalanced class data sets.